Then there is the whole digitisation of the grid. Now all new equipment is being built with inbuilt ‘smarts’ and connectivity, and even older infrastructure can be retrofitted, so with the advent of the smart grid, we will finally have the possibility of the Electricity 2.0 vision I was talking up back in 2008/09. This is a smart grid where appliances in the commercial or residential worlds can ‘listen’ for pricing signals from the grid, and adjust their behaviour accordingly, taking in electricity when it is plentiful, and switching to alternative sources/lowering consumption when electricity is in high demand.

Everything is changing for the electric utility industry – and so, against that backdrop, and the fact that I will be presenting on IoT and Utilities at the upcoming International SAP for Utilities Conference in Lisbon, I decided to have a chat with IDC Research Director Marcus Torchia, about the implications for utilities of these huge changes.

We had a great discussion, and many of the themes we touched on, I will be talking about at the Utilities event in Lisbon.

You can check out our chat in the video above, play it in the audio below, or listen to it on the IoT Heroes podcast site.

The data comes from April 26th this year through to Mar 3rd. The sever small graphs along the bottom are daily demand curves, going from Tuesday April 26th on the left, through to Monday May 3rd on the right. You can see that the demand curves for each day are virtually the same.

Saturday and Sunday are however, obvious due to the lower demand on those days, and if you are wondering why Monday the 3rd looks to be lower than the rest of the weekdays, it is because that Monday was a holiday in Spain.

The large graph on top is a zoomed-in look at the demand on one of those days – Friday April 29th. From that you can see that the demand starts to rise early in the morning with the peak occurring between 8-11am. Demand then falls off until late afternoon when people are cooking their evening meals, peaking around 9pm, and then falling until it starts again the following day.

The pattern varies slightly by day of the week, as well as by season, but overall while it is variable, it is also highly predictable.

Graph of predicted energy demand (Green) vs actual demand (yellow) on Spanish grid on April 29th this year – graph from REE

This can be problematic though when you have high penetrations of variable energy suppliers, such as wind and solar.

Here is the energy supplied to the system by wind, for example on April 29th

Wind energy on the 29th of April on the Spanish grid

As you can see, it doesn’t map well with the demand, and this is challenging for grid management companies, especially with increasing pressure on them to decarbonise.

That can lead to circumstances where wind power ends up supplying 140% of your demand, as happened in the Netherlands last summer. Fortunately, the Netherlands has good interconnects, and so was able to sell this excess energy to its neighbouring countries. This won’t always be the case though, and will become a more common issue as the penetration of wind and solar increases globally.

Obviously, if you can’t manage the supply side of the grid, what about managing the demand – how achievable is that?

Interestingly, this is now becoming a real possibility. Already there are companies who aggregate the demand of large organisations with facilities for reducing demand, if required, and sell that reduced demand to utility companies. This can save the utility from having to build new generation sources to meet the increased demand at times of peak load.

Demand flexibility

What if this were more widespread?

Looking at the chart above, if we could shift the yellow demand line up during its overnight dip, and then reduce the yellow demand line during the morning and evening, this would make the grid more stable, and allow for the introduction of more variable generators (solar and wind) onto the system, as well as reducing the requirement for expensive ‘peaker plants’.

Reduced, or negative pricing is a better option than wind farm curtailment because curtailment lowers the income for the wind farms, making them a less attractive investment for renewables developers, while reduced pricing moves the demand to a more suitable time.

Now, with the advent of the Internet of Things, everything starts to be smart and connected. If our electricity devices can listen for realtime electricity signals from the grid, they can adjust their consumption accordingly.

Of course, not all loads in the home are movable – not many people will decide to cook their evening meal at 3am just because the wind is blowing and energy is cheap.

However, many loads are eminently movable. Pool pumps, are a good example. And also many loads that have a heating or cooling component associated with them, such as an electric hot water heater. When it is well insulated it doesn’t matter when it heats the water. Similarly for fridges, freezers, ice bank air conditioning, and so on. These are straightforward and affordable forms of energy storage.

Dish washers, washing machines, clothes dryers can also be made to listen to electricity pricing, and adjust their behaviour accordingly. Often, when you put the dish washer on in the evening, you don’t care when it comes on, as long as the dishes are clean and dry when you get up the following morning.

As more of our appliances become connected and smart, this will become the norm. Obviously, for widespread adoption, this kind of behaviour has to be totally automated. If the device owner has to think about it, it won’t happen.

And then there are the real storage options, using batteries. This can be in the form of batteries in electric vehicles using vehicle-to-grid technologies, in-home batteries such as the ones Tesla, and others sell, or reconditioned electric vehicle batteries – a market that is just starting to get going.

So, good news, technology is moving us inexorably to a world where energy is getting cheaper, smarter, and less carbon intensive.